Le. D.C., Zincir-Heywood, N. and Heywood, M.I. (2020) Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning. IEEE Transactions on Network and Service Management, 17, 30-44.
https://doi.org/10.1109/TNSM.2020.2967721

has been cited by the following article:

• TITLE: Generative Adversarial Network Based Approach towards Synthetically Generating Insider Threat Scenarios

  AUTHORS: Mayesh Mohapatra, Anshumaan Phukan, Vijay K. Madisetti

  KEYWORDS: GANs, CERT, Insider-Threat, Cybersecurity

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.16 No.11, November 28, 2023

  ABSTRACT: This research paper explores the use of Generative Adversarial Networks (GANs) to synthetically generate insider threat scenarios. Insider threats pose significant risks to IT infrastructures, requiring effective detection and mitigation strategies. By training GAN models on historical insider threat data, synthetic scenarios resembling real-world incidents can be generated, including various tactics and procedures employed by insiders. The paper discusses the benefits, challenges, and ethical considerations associated with using GAN-generated data. The findings highlight the potential of GANs in enhancing insider threat detection and response capabilities, empowering organizations to fortify their defenses and proactively mitigate risks posed by internal actors.